Paper Citation Record · LEDGER

Assessing Adversarial Robustness of Large Language Models: An Empirical Study

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2405.02764.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2405.02764 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T23:21:41.816106Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-03T17:18:43.006113Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 5223ae79-5e8f-47b6-ab56-51e0d798337b · inbound

On Adversarial Robustness of Language Models in Transfer Learning cites this paper.

On Adversarial Robustness of Language Models in Transfer Learning Assessing Adversarial Robustness of Large Language Models: An Empirical Study

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-10T23:21:41.816106Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c123b6c1-bcc2-421d-9762-3a31f5cce95e · inbound

Bridging Robustness and Generalization Against Word Substitution Attacks in NLP via the Growth Bound Matrix Approach cites this paper.

Bridging Robustness and Generalization Against Word Substitution Attacks in NLP via the Growth Bound Matrix Approach Assessing Adversarial Robustness of Large Language Models: An Empirical Study

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-06T17:40:06.404790Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a4f9dc18-43ee-4f7b-b31c-87e0b6741f14 · inbound

Rethinking Testing for LLM Applications: Characteristics, Challenges, and a Lightweight Interaction Protocol cites this paper.

Rethinking Testing for LLM Applications: Characteristics, Challenges, and a Lightweight Interaction Protocol Assessing Adversarial Robustness of Large Language Models: An Empirical Study

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-05T14:55:57.051121Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6ff8606f-8ac0-4f89-ae67-29db416057dd · inbound

Are LLMs Ready for Conflict Monitoring? Empirical Evidence from West Africa cites this paper.

Are LLMs Ready for Conflict Monitoring? Empirical Evidence from West Africa Assessing Adversarial Robustness of Large Language Models: An Empirical Study

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-11T17:11:18.047201Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 16596775-87ed-4adc-8d22-bf6c3497a0de · inbound

S-GBT: Smooth Growth Bound Tensor for Certified Robustness Against Word Substitution Attacks in NLP cites this paper.

S-GBT: Smooth Growth Bound Tensor for Certified Robustness Against Word Substitution Attacks in NLP Assessing Adversarial Robustness of Large Language Models: An Empirical Study

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-07-03T14:08:22.542663Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 64946a7b-adb2-4187-b3cf-18b1304a06a9 · inbound

Do LLMs Truly Generalize in the Molecular Domain? A Perturbation-Based Analysis cites this paper.

Do LLMs Truly Generalize in the Molecular Domain? A Perturbation-Based Analysis Assessing Adversarial Robustness of Large Language Models: An Empirical Study

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-07-03T17:18:43.007917Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.